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pacman::p_load(arrow, lubridate, sf, tidyverse, spNetwork, tmap,
spatstat, raster, maptools)Take-home_Ex01
1. Objective
We will be applying appropriate spatial point patterns analysis methods learned in class to discover the geographical and spatio-temporal distribution of Grab hailing services locations in Singapore.
2. Getting Started
2.1 Loading R packages
The R packages that we will be using in this exercise are as follows:
arrow: For reading parquet files (Grab-Posisi Dataset)
lubridate: To handle the date formatting
sf: Import, manage and process vector-based geospatial data in R.
tidyverse: a collection of packages for data science tasks
spatstat: Wide range of useful functions for point pattern analysis and derive kernel density estimation (KDE) layer.
spNetwork: provides functions to perform Spatial Point Patterns Analysis such as kernel density estimation (KDE) and K-function on network. It also can be used to build spatial matrices (‘listw’ objects like in ‘spdep’ package) to conduct any kind of traditional spatial analysis with spatial weights based on reticular distances.
tmap: Provides functions for plotting cartographic quality static point patterns maps or interactive maps by using leaflet API.
raster: reads, writes, manipulates, analyses and model of gridded spatial data (i.e. raster). In this hands-on exercise, it will be used to convert image output generate by spatstat into raster format.
maptools: Provides a set of tools for manipulating geographic data. In this take-home exercise, we mainly use it to convert Spatial objects into ppp format of spatstat.
# classInt, viridis, rgdal
2.2 Importing the datasets
The datasets that we will be using are as follow:
Using read_parquet() function from arrow package to import the grab data, then changing pingtimestamp column to datetime object
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grab_df <- read_parquet("data/aspatial/part-00000.snappy.parquet")
grab_df$pingtimestamp <- as_datetime(grab_df$pingtimestamp)Transforming the coordinate system at the same time when we are importing the data
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sg_roads <- st_read(dsn = "data/geospatial", layer = "gis_osm_roads_free_1") %>% st_transform(crs = 3414)Reading layer `gis_osm_roads_free_1' from data source
`/Users/jacksontan/Documents/Sashimii0219/IS415-GAA/Take-home_Ex/Take-home_Ex01/data/geospatial'
using driver `ESRI Shapefile'
Simple feature collection with 1759836 features and 10 fields
Geometry type: LINESTRING
Dimension: XY
Bounding box: xmin: 99.66041 ymin: 0.8021131 xmax: 119.2601 ymax: 7.514393
Geodetic CRS: WGS 84Transforming the coordinate system at the same time when we are importing the data
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mpsz2019 <- st_read("data/geospatial", layer = "MPSZ-2019") %>% st_transform(crs = 3414)Reading layer `MPSZ-2019' from data source
`/Users/jacksontan/Documents/Sashimii0219/IS415-GAA/Take-home_Ex/Take-home_Ex01/data/geospatial'
using driver `ESRI Shapefile'
Simple feature collection with 332 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 103.6057 ymin: 1.158699 xmax: 104.0885 ymax: 1.470775
Geodetic CRS: WGS 843. Geospatial Data Wrangling
Before we begin exploring the data, we will first need to perform some data pre-processing on the datasets that we have imported.
3.1 Data Pre-processing - MPSZ2019
3.1.1 Excluding Outer Islands
As grab won’t be able to reach offshore places, we will exclude the outer islands from this dataset. We will do this through the following steps:
We will first take a look at the unique planning areas in Singapore using unique() on the PLN_AREA_N column of mpsz2019 dataset.
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unique(mpsz2019$PLN_AREA_N) [1] "MARINA EAST" "RIVER VALLEY"
[3] "SINGAPORE RIVER" "WESTERN ISLANDS"
[5] "MUSEUM" "MARINE PARADE"
[7] "SOUTHERN ISLANDS" "BUKIT MERAH"
[9] "DOWNTOWN CORE" "STRAITS VIEW"
[11] "QUEENSTOWN" "OUTRAM"
[13] "MARINA SOUTH" "ROCHOR"
[15] "KALLANG" "TANGLIN"
[17] "NEWTON" "CLEMENTI"
[19] "BEDOK" "PIONEER"
[21] "JURONG EAST" "ORCHARD"
[23] "GEYLANG" "BOON LAY"
[25] "BUKIT TIMAH" "NOVENA"
[27] "TOA PAYOH" "TUAS"
[29] "JURONG WEST" "SERANGOON"
[31] "BISHAN" "TAMPINES"
[33] "BUKIT BATOK" "HOUGANG"
[35] "CHANGI BAY" "PAYA LEBAR"
[37] "ANG MO KIO" "PASIR RIS"
[39] "BUKIT PANJANG" "TENGAH"
[41] "SELETAR" "SUNGEI KADUT"
[43] "YISHUN" "MANDAI"
[45] "PUNGGOL" "CHOA CHU KANG"
[47] "SENGKANG" "CHANGI"
[49] "CENTRAL WATER CATCHMENT" "SEMBAWANG"
[51] "WESTERN WATER CATCHMENT" "WOODLANDS"
[53] "NORTH-EASTERN ISLANDS" "SIMPANG"
[55] "LIM CHU KANG" Show code
plot(mpsz2019)
Note that there are 3 areas with island in their name, mainly “NORTH-EASTERN ISLANDS”, “SOUTHERN ISLANDS”, and “WESTERN ISLANDS”.
To exclude the islands, we simply have to pass a condition to exclude these islands in the subset function.
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mpsz2019_new <- subset(mpsz2019, !(PLN_AREA_N %in%
c("NORTH-EASTERN ISLANDS", "SOUTHERN ISLANDS", "WESTERN ISLANDS")))Great! Now let’s check if we indeed removed the maps!
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tmap_mode('plot')
before <- tm_shape(mpsz2019) +
tm_polygons("PLN_AREA_N") +
tmap_options(max.categories = 53)
after <- tm_shape(mpsz2019_new) +
tm_polygons("PLN_AREA_N") +
tmap_options(max.categories = 53)
tmap_arrange(before, after)
3.1.2 Invalid Geometries
We will be using the st_is_valid() function to test for invalid geometries.
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test <- st_is_valid(mpsz2019_new,reason=TRUE)
# Number of invalid geometries
length(which(test!= "Valid Geometry"))[1] 3Show code
# Reason
test[which(test!= "Valid Geometry")][1] "Ring Self-intersection[26922.5243000389 27027.610899987]"
[2] "Ring Self-intersection[38991.2589000446 31986.5599999869]"
[3] "Ring Self-intersection[14484.6860000313 31330.1319999856]"We can see that there are 3 invalid geometries. Let’s fix them using st_make_valid().
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mpsz2019_new<- st_make_valid(mpsz2019_new)
length(which(st_is_valid(mpsz2019_new) == FALSE))[1] 03.1.3 Missing Values
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mpsz2019_new[rowSums(is.na(mpsz2019_new))!=0,]Simple feature collection with 0 features and 6 fields
Bounding box: xmin: NA ymin: NA xmax: NA ymax: NA
Projected CRS: SVY21 / Singapore TM
[1] SUBZONE_N SUBZONE_C PLN_AREA_N PLN_AREA_C REGION_N REGION_C geometry
<0 rows> (or 0-length row.names)Using the code above, we can see that there are no missing values.
3.1.4 Creating boundary?
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sg_boundary <- mpsz2019_new %>% st_union()
plot(sg_boundary)
3.2 Data Pre-processing - OpenStreetMap Road Dataset
3.2.1 Limiting the dataset
As the dataset contains data from Malaysia and Brunei as well, we will use st_intersection() to limit the data to only Singapore.
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points_within_sg <- st_intersection(sg_roads, mpsz2019_new)Now, we can see that in points_within_sg it only contain Singapore road data, combined with the other values from mpsz2019 like “PLN_AREA_N” used above.
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colnames(points_within_sg) [1] "osm_id" "code" "fclass" "name" "ref"
[6] "oneway" "maxspeed" "layer" "bridge" "tunnel"
[11] "SUBZONE_N" "SUBZONE_C" "PLN_AREA_N" "PLN_AREA_C" "REGION_N"
[16] "REGION_C" "geometry" Show code
head(points_within_sg)Simple feature collection with 6 features and 16 fields
Geometry type: LINESTRING
Dimension: XY
Bounding box: xmin: 31466.72 ymin: 30680.54 xmax: 32815.21 ymax: 30873.74
Projected CRS: SVY21 / Singapore TM
osm_id code fclass name ref oneway maxspeed layer
4052 23946437 5122 residential Rhu Cross <NA> F 50 0
9668 32605139 5131 motorway_link <NA> <NA> F 40 0
20076 46337834 5131 motorway_link <NA> <NA> F 50 -2
21690 49961799 5111 motorway East Coast Parkway ECP F 70 1
26543 74722808 5111 motorway East Coast Parkway ECP F 70 1
29808 99007260 5131 motorway_link <NA> <NA> F 50 1
bridge tunnel SUBZONE_N SUBZONE_C PLN_AREA_N PLN_AREA_C REGION_N
4052 F F MARINA EAST MESZ01 MARINA EAST ME CENTRAL REGION
9668 F F MARINA EAST MESZ01 MARINA EAST ME CENTRAL REGION
20076 F T MARINA EAST MESZ01 MARINA EAST ME CENTRAL REGION
21690 F F MARINA EAST MESZ01 MARINA EAST ME CENTRAL REGION
26543 T F MARINA EAST MESZ01 MARINA EAST ME CENTRAL REGION
29808 T F MARINA EAST MESZ01 MARINA EAST ME CENTRAL REGION
REGION_C geometry
4052 CR LINESTRING (31889.45 30760....
9668 CR LINESTRING (32768.57 30857....
20076 CR LINESTRING (32815.21 30873....
21690 CR LINESTRING (32365.45 30845....
26543 CR LINESTRING (31611.63 30720....
29808 CR LINESTRING (31611.63 30720....3.2.2 Invalid Geometries
Again, using the st_is_valid() function to test for invalid geometries.
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test <- st_is_valid(points_within_sg,reason=TRUE)
# Number of invalid geometries
length(which(test!= "Valid Geometry"))[1] 0Show code
# Reason
test[which(test!= "Valid Geometry")]character(0)No invalid geometries!
3.2.3 Missing Values / Dropping Columns
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points_within_sg[rowSums(is.na(points_within_sg))!=0,]Simple feature collection with 232766 features and 16 fields
Geometry type: GEOMETRY
Dimension: XY
Bounding box: xmin: 2679.373 ymin: 23099.51 xmax: 50957.8 ymax: 50220.06
Projected CRS: SVY21 / Singapore TM
First 10 features:
osm_id code fclass name ref oneway maxspeed layer
4052 23946437 5122 residential Rhu Cross <NA> F 50 0
9668 32605139 5131 motorway_link <NA> <NA> F 40 0
20076 46337834 5131 motorway_link <NA> <NA> F 50 -2
29808 99007260 5131 motorway_link <NA> <NA> F 50 1
45723 140562813 5131 motorway_link <NA> <NA> F 70 -1
45728 140562819 5131 motorway_link <NA> <NA> F 50 0
45731 140562823 5131 motorway_link <NA> <NA> F 60 -2
45733 140562826 5131 motorway_link <NA> <NA> F 40 0
52966 150819034 5141 service Bay East Drive <NA> B 0 0
84664 174717984 5153 footway <NA> <NA> B 0 0
bridge tunnel SUBZONE_N SUBZONE_C PLN_AREA_N PLN_AREA_C REGION_N
4052 F F MARINA EAST MESZ01 MARINA EAST ME CENTRAL REGION
9668 F F MARINA EAST MESZ01 MARINA EAST ME CENTRAL REGION
20076 F T MARINA EAST MESZ01 MARINA EAST ME CENTRAL REGION
29808 T F MARINA EAST MESZ01 MARINA EAST ME CENTRAL REGION
45723 F F MARINA EAST MESZ01 MARINA EAST ME CENTRAL REGION
45728 F F MARINA EAST MESZ01 MARINA EAST ME CENTRAL REGION
45731 F T MARINA EAST MESZ01 MARINA EAST ME CENTRAL REGION
45733 F F MARINA EAST MESZ01 MARINA EAST ME CENTRAL REGION
52966 F F MARINA EAST MESZ01 MARINA EAST ME CENTRAL REGION
84664 F F MARINA EAST MESZ01 MARINA EAST ME CENTRAL REGION
REGION_C geometry
4052 CR LINESTRING (31889.45 30760....
9668 CR LINESTRING (32768.57 30857....
20076 CR LINESTRING (32815.21 30873....
29808 CR LINESTRING (31611.63 30720....
45723 CR LINESTRING (32782.42 30754....
45728 CR LINESTRING (32645.37 30683....
45731 CR LINESTRING (32809.68 30108....
45733 CR LINESTRING (32609.11 30700....
52966 CR LINESTRING (32173.46 30036....
84664 CR LINESTRING (31750.06 30644....By using the code above, we can see that majority of the missing values are in the ‘name’ and ‘ref’ column. Therefore, let’s drop the irrelevant columns first before we try it again!
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sg_roads_new <- points_within_sg[c("osm_id", "code", "fclass", "PLN_AREA_N", "geometry")]We only kept “osm_id”, “code”, “fclass”, and “PLN_AREA_N” columns.
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sg_roads_new[rowSums(is.na(sg_roads_new))!=0,]Simple feature collection with 0 features and 4 fields
Bounding box: xmin: NA ymin: NA xmax: NA ymax: NA
Projected CRS: SVY21 / Singapore TM
[1] osm_id code fclass PLN_AREA_N geometry
<0 rows> (or 0-length row.names)No more missing values here.
Our map so far:
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tm_shape(sg_boundary) +
tm_polygons() +
tm_shape(sg_roads_new) +
tm_lines("PLN_AREA_N")
3.3 Data Pre-processing - Grab-Posisi Dataset
The Grab-Posisi Dataset is an Aspatial dataset, different from the two we prepared above. As such, the pre-processing is slightly different too.
3.3.1 Getting the Origin and Destination Locations
The code below is a chain of dplyr pipes to group the trips by their id and extract the first pingtimestamp row of each trip in order to get the origin of it.
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origin_df <- grab_df %>%
group_by(trj_id) %>%
arrange(pingtimestamp) %>%
filter(row_number()==1) %>%
mutate(weekday = wday(pingtimestamp,
label=TRUE,
abbr=TRUE),
start_hr = factor(hour(pingtimestamp)),
day = factor(mday(pingtimestamp)))Show code
destination_df <- grab_df %>%
group_by(trj_id) %>%
arrange(desc(pingtimestamp)) %>%
# Same as previous code but desc, so ending location
filter(row_number()==1) %>%
mutate(weekday = wday(pingtimestamp,
label=TRUE,
abbr=TRUE),
end_hr = factor(hour(pingtimestamp)),
day = factor(mday(pingtimestamp)))3.3.2 Converting to SF format from Dataframe
We will need the files in SF format first before we can use it for further geospatial analysis.
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origin_sf <- st_as_sf(origin_df,
coords = c("rawlng", "rawlat"),
crs=4326) %>%
st_transform(crs = 3414)
dest_sf <- st_as_sf(destination_df,
coords = c("rawlng", "rawlat"),
crs=4326) %>%
st_transform(crs = 3414)3.3.3 Invalid Geometries
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test <- st_is_valid(origin_sf,reason=TRUE)
length(which(test!= "Valid Geometry"))[1] 0Show code
test <- st_is_valid(dest_sf,reason=TRUE)
length(which(test!= "Valid Geometry"))[1] 03.3.4 Missing Files
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origin_sf[rowSums(is.na(origin_sf))!=0,]Simple feature collection with 0 features and 10 fields
Bounding box: xmin: NA ymin: NA xmax: NA ymax: NA
Projected CRS: SVY21 / Singapore TM
# A tibble: 0 × 11
# Groups: trj_id [0]
# ℹ 11 variables: trj_id <chr>, driving_mode <chr>, osname <chr>,
# pingtimestamp <dttm>, speed <dbl>, bearing <int>, accuracy <dbl>,
# weekday <ord>, start_hr <fct>, day <fct>, geometry <GEOMETRY [m]>Show code
dest_sf[rowSums(is.na(dest_sf))!=0,]Simple feature collection with 0 features and 10 fields
Bounding box: xmin: NA ymin: NA xmax: NA ymax: NA
Projected CRS: SVY21 / Singapore TM
# A tibble: 0 × 11
# Groups: trj_id [0]
# ℹ 11 variables: trj_id <chr>, driving_mode <chr>, osname <chr>,
# pingtimestamp <dttm>, speed <dbl>, bearing <int>, accuracy <dbl>,
# weekday <ord>, end_hr <fct>, day <fct>, geometry <GEOMETRY [m]>No missing values, we are almost ready.
3.3.5 Removing points on the islands
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origin_sf_new <- st_intersection(origin_sf, mpsz2019_new)
dest_sf_new <- st_intersection(dest_sf, mpsz2019_new)To verify that the points that we removed is indeed from the islands, here’s a chunk of code to prove:
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# Finding out points removed
diff_id <- origin_sf$trj_id[!(origin_sf$trj_id %in% origin_sf_new$trj_id)]
# Extracting full information of these points
outliers <- origin_sf[(origin_sf$trj_id %in% diff_id), ]
# Checking where these places are from
unique(st_intersection(outliers, mpsz2019)$PLN_AREA_N)[1] "WESTERN ISLANDS" "SOUTHERN ISLANDS"They are indeed from “WESTERN ISLANDS” and “SOUTHERN ISLANDS”.
3.3.6 Dropping Unnecessary Columns
Now that our grab dataset is almost ready, we need to decide which column we should drop. Here are the columns in both origin_sf_new and dest_sf_new:
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colnames(origin_sf_new) [1] "trj_id" "driving_mode" "osname" "pingtimestamp"
[5] "speed" "bearing" "accuracy" "weekday"
[9] "start_hr" "day" "SUBZONE_N" "SUBZONE_C"
[13] "PLN_AREA_N" "PLN_AREA_C" "REGION_N" "REGION_C"
[17] "geometry" Show code
colnames(dest_sf_new) [1] "trj_id" "driving_mode" "osname" "pingtimestamp"
[5] "speed" "bearing" "accuracy" "weekday"
[9] "end_hr" "day" "SUBZONE_N" "SUBZONE_C"
[13] "PLN_AREA_N" "PLN_AREA_C" "REGION_N" "REGION_C"
[17] "geometry" We will definitely be dropping the columns merged from mpsz2019_new (other than PLN_AREA_N), but what about “driving_mode”, “osname”, “speed”, “bearing”, and “accuracy”? Let’s first take a look at them.
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unique(origin_sf_new$driving_mode)[1] "car"Seeing that there is only 1 constant in the column, it is safe for us to drop this column.
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unique(origin_sf_new$osname)[1] "ios" "android"There are 2 values, mainly “ios” and “android”. Arguments can be made that we can analyse the behavior of both type in terms of using grab hailing services, but that’s not what we will doing so we will drop this as well.
As we are analysing start/stop points, speed will not be a relevant factor hence we will be dropping them.
Not relevant as well, hence dropping.
According to research paper published on Grab website, this is the definition of the accuracy column:
“…the accuracy level roughly indicates the radius of the circle within which the true location lies with a certain probability. The lower the accuracy level, the more precise the reported GPS ping is.”
With that, let’s take a look at the distribution of accuracy score.
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plot(origin_sf_new$accuracy)
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ggplot(origin_sf_new,
aes(x=rownames(origin_sf_new), y=accuracy)) +
geom_point(size = 2)
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ggplot(dest_sf_new,
aes(x=rownames(dest_sf_new), y=accuracy)) +
geom_point(size = 2)
From the plot, we can see that there are 3 clear outliers with accuracy above 180~ for origin_sf_new, and 1 for dest_sf_new. Now let’s extract these trips.
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origin_sf_new[origin_sf_new$accuracy > 180, ]Simple feature collection with 3 features and 16 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 18132.25 ymin: 30203 xmax: 28937.76 ymax: 36948.91
Projected CRS: SVY21 / Singapore TM
# A tibble: 3 × 17
trj_id driving_mode osname pingtimestamp speed bearing accuracy
<chr> <chr> <chr> <dttm> <dbl> <int> <dbl>
1 78815 car ios 2019-04-21 13:20:13 0.000000101 68 200
2 67866 car ios 2019-04-18 16:46:16 -1 13 547
3 4579 car ios 2019-04-21 10:35:22 13.0 108 200
# ℹ 10 more variables: weekday <ord>, start_hr <fct>, day <fct>,
# SUBZONE_N <chr>, SUBZONE_C <chr>, PLN_AREA_N <chr>, PLN_AREA_C <chr>,
# REGION_N <chr>, REGION_C <chr>, geometry <POINT [m]>Show code
dest_sf_new[dest_sf_new$accuracy > 500, ]Simple feature collection with 1 feature and 16 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 33721.09 ymin: 34502.5 xmax: 33721.09 ymax: 34502.5
Projected CRS: SVY21 / Singapore TM
# A tibble: 1 × 17
trj_id driving_mode osname pingtimestamp speed bearing accuracy weekday
<chr> <chr> <chr> <dttm> <dbl> <int> <dbl> <ord>
1 68340 car ios 2019-04-12 11:55:48 -1 10 1414 Fri
# ℹ 9 more variables: end_hr <fct>, day <fct>, SUBZONE_N <chr>,
# SUBZONE_C <chr>, PLN_AREA_N <chr>, PLN_AREA_C <chr>, REGION_N <chr>,
# REGION_C <chr>, geometry <POINT [m]>To ensure that our data is of utmost accuracy, we will drop these trips, before we drop the accuracy column as well (as we will not need it anymore).
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origin_sf_new <- subset(origin_sf_new, accuracy < 180)
dest_sf_new <- subset(dest_sf_new, accuracy < 500)With that done, we can now drop the columns that we don’t need.
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origin_sf_new <- origin_sf_new[, c(1, 4, 8:10, 13, 17)]
dest_sf_new <- dest_sf_new[, c(1, 4, 8:10, 13, 17)]3.3.7 Duplicated Points
Lastly, let’s check for duplicated points on the map.
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# Check for any duplicates
any(duplicated(origin_sf_new))[1] FALSEShow code
# Count the number of duplicates
sum(multiplicity(origin_sf_new) > 1)[1] 0No duplicated points!
3.4 Verifying Coordinate System
It is important for the data to be in the right coordinate reference system (CRS). In this assignment, all spatial data will be projected in EPSG:3414, which is a projected coordinate system for Singapore.
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st_crs(mpsz2019_new)Coordinate Reference System:
User input: EPSG:3414
wkt:
PROJCRS["SVY21 / Singapore TM",
BASEGEOGCRS["SVY21",
DATUM["SVY21",
ELLIPSOID["WGS 84",6378137,298.257223563,
LENGTHUNIT["metre",1]]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
ID["EPSG",4757]],
CONVERSION["Singapore Transverse Mercator",
METHOD["Transverse Mercator",
ID["EPSG",9807]],
PARAMETER["Latitude of natural origin",1.36666666666667,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8801]],
PARAMETER["Longitude of natural origin",103.833333333333,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8802]],
PARAMETER["Scale factor at natural origin",1,
SCALEUNIT["unity",1],
ID["EPSG",8805]],
PARAMETER["False easting",28001.642,
LENGTHUNIT["metre",1],
ID["EPSG",8806]],
PARAMETER["False northing",38744.572,
LENGTHUNIT["metre",1],
ID["EPSG",8807]]],
CS[Cartesian,2],
AXIS["northing (N)",north,
ORDER[1],
LENGTHUNIT["metre",1]],
AXIS["easting (E)",east,
ORDER[2],
LENGTHUNIT["metre",1]],
USAGE[
SCOPE["Cadastre, engineering survey, topographic mapping."],
AREA["Singapore - onshore and offshore."],
BBOX[1.13,103.59,1.47,104.07]],
ID["EPSG",3414]]Show code
st_crs(sg_roads_new)Coordinate Reference System:
User input: EPSG:3414
wkt:
PROJCRS["SVY21 / Singapore TM",
BASEGEOGCRS["SVY21",
DATUM["SVY21",
ELLIPSOID["WGS 84",6378137,298.257223563,
LENGTHUNIT["metre",1]]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
ID["EPSG",4757]],
CONVERSION["Singapore Transverse Mercator",
METHOD["Transverse Mercator",
ID["EPSG",9807]],
PARAMETER["Latitude of natural origin",1.36666666666667,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8801]],
PARAMETER["Longitude of natural origin",103.833333333333,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8802]],
PARAMETER["Scale factor at natural origin",1,
SCALEUNIT["unity",1],
ID["EPSG",8805]],
PARAMETER["False easting",28001.642,
LENGTHUNIT["metre",1],
ID["EPSG",8806]],
PARAMETER["False northing",38744.572,
LENGTHUNIT["metre",1],
ID["EPSG",8807]]],
CS[Cartesian,2],
AXIS["northing (N)",north,
ORDER[1],
LENGTHUNIT["metre",1]],
AXIS["easting (E)",east,
ORDER[2],
LENGTHUNIT["metre",1]],
USAGE[
SCOPE["Cadastre, engineering survey, topographic mapping."],
AREA["Singapore - onshore and offshore."],
BBOX[1.13,103.59,1.47,104.07]],
ID["EPSG",3414]]Show code
st_crs(origin_sf_new)Coordinate Reference System:
User input: EPSG:3414
wkt:
PROJCRS["SVY21 / Singapore TM",
BASEGEOGCRS["SVY21",
DATUM["SVY21",
ELLIPSOID["WGS 84",6378137,298.257223563,
LENGTHUNIT["metre",1]]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
ID["EPSG",4757]],
CONVERSION["Singapore Transverse Mercator",
METHOD["Transverse Mercator",
ID["EPSG",9807]],
PARAMETER["Latitude of natural origin",1.36666666666667,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8801]],
PARAMETER["Longitude of natural origin",103.833333333333,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8802]],
PARAMETER["Scale factor at natural origin",1,
SCALEUNIT["unity",1],
ID["EPSG",8805]],
PARAMETER["False easting",28001.642,
LENGTHUNIT["metre",1],
ID["EPSG",8806]],
PARAMETER["False northing",38744.572,
LENGTHUNIT["metre",1],
ID["EPSG",8807]]],
CS[Cartesian,2],
AXIS["northing (N)",north,
ORDER[1],
LENGTHUNIT["metre",1]],
AXIS["easting (E)",east,
ORDER[2],
LENGTHUNIT["metre",1]],
USAGE[
SCOPE["Cadastre, engineering survey, topographic mapping."],
AREA["Singapore - onshore and offshore."],
BBOX[1.13,103.59,1.47,104.07]],
ID["EPSG",3414]]Show code
st_crs(dest_sf_new)Coordinate Reference System:
User input: EPSG:3414
wkt:
PROJCRS["SVY21 / Singapore TM",
BASEGEOGCRS["SVY21",
DATUM["SVY21",
ELLIPSOID["WGS 84",6378137,298.257223563,
LENGTHUNIT["metre",1]]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
ID["EPSG",4757]],
CONVERSION["Singapore Transverse Mercator",
METHOD["Transverse Mercator",
ID["EPSG",9807]],
PARAMETER["Latitude of natural origin",1.36666666666667,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8801]],
PARAMETER["Longitude of natural origin",103.833333333333,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8802]],
PARAMETER["Scale factor at natural origin",1,
SCALEUNIT["unity",1],
ID["EPSG",8805]],
PARAMETER["False easting",28001.642,
LENGTHUNIT["metre",1],
ID["EPSG",8806]],
PARAMETER["False northing",38744.572,
LENGTHUNIT["metre",1],
ID["EPSG",8807]]],
CS[Cartesian,2],
AXIS["northing (N)",north,
ORDER[1],
LENGTHUNIT["metre",1]],
AXIS["easting (E)",east,
ORDER[2],
LENGTHUNIT["metre",1]],
USAGE[
SCOPE["Cadastre, engineering survey, topographic mapping."],
AREA["Singapore - onshore and offshore."],
BBOX[1.13,103.59,1.47,104.07]],
ID["EPSG",3414]]They are all in the correct CRS!
3.5 Plotting Spatial Data
Finally, plotting all three datasets together to ensure that they have a consistent projection system.
Show code
tm_shape(sg_boundary) +
tm_polygons() +
tm_shape(sg_roads_new) +
tm_lines("PLN_AREA_N") +
tm_shape(origin_sf_new) +
tm_dots()
3.6 Exploratory Data Analysis
Before we begin our Geospatial Analysis, let’s first take a closer look at the Grab dataset.
3.6.1 Day of the Week
The distribution of the trips across all 7 days of the week looks even.
Show code
ggplot(origin_sf_new, aes(x=weekday)) + geom_bar()
Show code
ggplot(dest_sf_new, aes(x=weekday)) + geom_bar()
3.6.2 Planning Area
First let us look at the top 10 planning areas for grab ride origin points. Tampines is the Planning Area with the most origin points.
Show code
origin_pl_area <- origin_sf_new %>%
group_by(PLN_AREA_N) %>%
summarise(total_count=n()) %>%
top_n(10, total_count) %>%
.$PLN_AREA_N
ggplot(origin_sf_new[origin_sf_new$PLN_AREA_N %in% origin_pl_area,],
aes(x=PLN_AREA_N)) + geom_bar() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
labs(title = "Trips Origin Distribution by Planning Area",
x = "Planning Area",
y = "Number of Trips")
Then for the destination points.
Show code
dest_pl_area <- dest_sf_new %>%
group_by(PLN_AREA_N) %>%
summarise(total_count=n()) %>%
top_n(10, total_count) %>%
.$PLN_AREA_N
ggplot(dest_sf_new[dest_sf_new$PLN_AREA_N %in% dest_pl_area,],
aes(x=PLN_AREA_N)) + geom_bar() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
labs(title = "Trips Destination Distribution by Planning Area",
x = "Planning Area",
y = "Number of Trips")
6 out of 10 of the Planning Areas remains the same for destination points, mainly TAMPINES, WOODLANDS, YISHUN, QUEENSTOWN, BUKIT MERAH, and CHANGI. This time however, the Planning Area with the most destination points is Changi.
3.6.3 Starting Hour
Show code
origin_sf_new$start_hr <- factor(origin_sf_new$start_hr, levels = 0:23)
ggplot(origin_sf_new, aes(x = start_hr)) +
geom_bar() +
labs(title = "Trips Distribution by Start Hour",
x = "Start Hour",
y = "Number of Trips")
From the graph, we can see that the starting hour peaks at midnight (12am - 1am) and morning (9am - 10am), the former probably due to the lack of public transport after operating hours, and the latter from rush hour.
4. Kernel Density Estimation (KDE) Layers
4.1 Converting data format
4.1.1 Creating point ppp objects
In the code chunk below, as.ppp() function is used to derive a ppp object layer directly from a sf tibble data.frame.
Show code
origin_ppp <- as.ppp(origin_sf_new)
summary(origin_ppp)Marked planar point pattern: 27872 points
Average intensity 2.636568e-05 points per square unit
Coordinates are given to 3 decimal places
i.e. rounded to the nearest multiple of 0.001 units
marks are of type 'character'
Summary:
Length Class Mode
27872 character character
Window: rectangle = [3661.47, 49845.23] x [26795.39, 49685.08] units
(46180 x 22890 units)
Window area = 1057130000 square unitsShow code
dest_ppp <- as.ppp(dest_sf_new)
summary(dest_ppp)Marked planar point pattern: 27820 points
Average intensity 2.642188e-05 points per square unit
Coordinates are given to 3 decimal places
i.e. rounded to the nearest multiple of 0.001 units
marks are of type 'character'
Summary:
Length Class Mode
27820 character character
Window: rectangle = [3638.69, 50024.92] x [26770.54, 49469.41] units
(46390 x 22700 units)
Window area = 1052920000 square units4.1.2 Creating owin objects
In the code chunk as.owin() is used to create an owin object class from polygon sf tibble data.frame. In this case, we will be converting the sg_boundary polygon.
Show code
sg_boundary_owin <- as.owin(sg_boundary)4.1.3 Combining point events object and owin object
We will now combine singapore’s boundary and the origin and destination points into one.
Show code
originSG_ppp = origin_ppp[sg_boundary_owin]
destSG_ppp = dest_ppp[sg_boundary_owin]Show code
plot(destSG_ppp)
Show code
summary(destSG_ppp)Marked planar point pattern: 27820 points
Average intensity 4.185996e-05 points per square unit
Coordinates are given to 3 decimal places
i.e. rounded to the nearest multiple of 0.001 units
marks are of type 'character'
Summary:
Length Class Mode
27820 character character
Window: polygonal boundary
37 separate polygons (29 holes)
vertices area relative.area
polygon 1 12666 6.63014e+08 9.98e-01
polygon 2 285 1.61128e+06 2.42e-03
polygon 3 27 1.50315e+04 2.26e-05
polygon 4 (hole) 41 -4.01660e+04 -6.04e-05
polygon 5 (hole) 317 -5.11280e+04 -7.69e-05
polygon 6 (hole) 3 -4.14099e-04 -6.23e-13
polygon 7 30 2.80002e+04 4.21e-05
polygon 8 (hole) 4 -2.86396e-01 -4.31e-10
polygon 9 (hole) 3 -1.81439e-04 -2.73e-13
polygon 10 (hole) 3 -8.68789e-04 -1.31e-12
polygon 11 (hole) 3 -5.99535e-04 -9.02e-13
polygon 12 (hole) 3 -3.04561e-04 -4.58e-13
polygon 13 (hole) 3 -4.46076e-04 -6.71e-13
polygon 14 (hole) 3 -3.39794e-04 -5.11e-13
polygon 15 (hole) 3 -4.52043e-05 -6.80e-14
polygon 16 (hole) 3 -3.90173e-05 -5.87e-14
polygon 17 (hole) 3 -9.59850e-05 -1.44e-13
polygon 18 (hole) 4 -2.54488e-04 -3.83e-13
polygon 19 (hole) 4 -4.28453e-01 -6.45e-10
polygon 20 (hole) 4 -2.18616e-04 -3.29e-13
polygon 21 (hole) 5 -2.44411e-04 -3.68e-13
polygon 22 (hole) 5 -3.64686e-02 -5.49e-11
polygon 23 71 8.18750e+03 1.23e-05
polygon 24 (hole) 6 -8.37554e-01 -1.26e-09
polygon 25 (hole) 38 -7.79904e+03 -1.17e-05
polygon 26 (hole) 3 -3.41897e-05 -5.14e-14
polygon 27 (hole) 3 -3.65499e-03 -5.50e-12
polygon 28 (hole) 3 -4.95057e-02 -7.45e-11
polygon 29 91 1.49663e+04 2.25e-05
polygon 30 (hole) 5 -2.92235e-04 -4.40e-13
polygon 31 (hole) 3 -7.43616e-06 -1.12e-14
polygon 32 (hole) 270 -1.21455e+03 -1.83e-06
polygon 33 (hole) 19 -4.39650e+00 -6.62e-09
polygon 34 (hole) 35 -1.38385e+02 -2.08e-07
polygon 35 (hole) 23 -1.99656e+01 -3.00e-08
polygon 36 71 5.63061e+03 8.47e-06
polygon 37 10 1.99717e+02 3.01e-07
enclosing rectangle: [2667.54, 55941.94] x [21448.47, 50256.33] units
(53270 x 28810 units)
Window area = 664597000 square units
Fraction of frame area: 0.4334.1.4 Rescale
The density values of the output range from 0 to 0.000035 which is way too small to comprehend, and it is computed in “number of points per square meter”. Therefore, we are going to use rescale() to covert the unit of measurement from meter to kilometer.
Show code
originSG_ppp.km <- rescale(originSG_ppp, 1000, "km")
destSG_ppp.km <- rescale(destSG_ppp, 1000, "km")4.2 Deriving Traditional Kernel Density Estimation (KDE) Layers
4.2.1 Automatic bandwidth selection method
We will first compute the kernel density by using density() of the spatstat package, with the default method bw.diggle().
Show code
kde_originSG_bw <- density(originSG_ppp.km,
sigma=bw.ppl,
edge=TRUE,
kernel="gaussian")
plot(kde_originSG_bw, main = "Kernel Density Estimation Layer")
Looking at all the different methods, we can see that bw.diggle() is still the best among the automatic bandwidth selection method.
Show code
bw.CvL(originSG_ppp.km) sigma
1.542325 Show code
bw.scott(originSG_ppp.km) sigma.x sigma.y
1.5924296 0.9284281 Show code
bw.ppl(originSG_ppp.km) sigma
0.08744077 Show code
bw.diggle(originSG_ppp.km) sigma
0.01078274 Show code
kde_originSG_ppl <- density(originSG_ppp.km,
sigma=bw.ppl,
edge=TRUE,
kernel="gaussian")
par(mfrow=c(1,2))
plot(kde_originSG_bw, main = "bw.diggle")
plot(kde_originSG_ppl, main = "bw.ppl")
4.2.2 Computing KDE by using fixed bandwidth
Having tried automatic bandwidth selection method, let’s try computing KDE by using a fixed bandwidth defined by us. In our case, we will define a fixed bandwidth of 800m (or 0.8km).
Show code
kde_originSG_500 <- density(originSG_ppp.km, sigma=0.5, edge=TRUE, kernel="gaussian")
plot(kde_originSG_500)
4.2.3 Computing KDE by using adaptive bandwidth
Fixed bandwidth method, however, is very sensitive to highly skewed distribution of spatial point patterns over geographical units, for example urban versus rural. To overcome this, we can try using adaptive bandwidth instead.
Show code
kde_childcareSG_adaptive <- adaptive.density(originSG_ppp.km, method="kernel")
plot(kde_childcareSG_adaptive)
4.2.4 Method we are using
As the KDE layer using fixed bandwidth with gaussian kernel plots a graph that allows for meaningful analysis at a glance, we will be using that for the steps moving forward.
Show code
kde_originSG_500 <- density(originSG_ppp.km, sigma=0.5, edge=TRUE, kernel="gaussian")
plot(kde_originSG_500)
Show code
kde_destSG_500 <- density(destSG_ppp.km, sigma=0.5, edge=TRUE, kernel="gaussian")
plot(kde_destSG_500)
Show code
par(mfrow=c(1,2))
plot(kde_originSG_500, main = "Origin KDE Layer")
plot(kde_destSG_500, main = "Destination KDE layer")
4.3 Combining KDE layers
4.3.1 Converting KDE layers into grid object
In order for us to map the KDE layer of these points to our map, we first need to convert it into grid object.
Show code
gridded_kde_originSG_500 <- as.SpatialGridDataFrame.im(kde_originSG_500)
spplot(gridded_kde_originSG_500)
Show code
gridded_kde_destSG_500 <- as.SpatialGridDataFrame.im(kde_destSG_500)
spplot(gridded_kde_destSG_500)
4.3.2 Converting KDE layers into grid object
We will then convert the gridded kernel density objects into RasterLayer object by using raster() of raster package. As the RasterLayer object does not include CRS information, we will need to manually assign it to them as well.
Show code
kde_originSG_500_raster <- raster(gridded_kde_originSG_500)
projection(kde_originSG_500_raster) <- CRS("+init=EPSG:3414 +datum=WGS84 +units=km")
kde_originSG_500_rasterclass : RasterLayer
dimensions : 128, 128, 16384 (nrow, ncol, ncell)
resolution : 0.4162063, 0.2250614 (x, y)
extent : 2.667538, 55.94194, 21.44847, 50.25633 (xmin, xmax, ymin, ymax)
crs : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +datum=WGS84 +units=km +no_defs
source : memory
names : v
values : -1.923671e-14, 596.2208 (min, max)Show code
kde_destSG_500_raster <- raster(gridded_kde_destSG_500)
projection(kde_destSG_500_raster) <- CRS("+init=EPSG:3414 +datum=WGS84 +units=km")
kde_destSG_500_rasterclass : RasterLayer
dimensions : 128, 128, 16384 (nrow, ncol, ncell)
resolution : 0.4162063, 0.2250614 (x, y)
extent : 2.667538, 55.94194, 21.44847, 50.25633 (xmin, xmax, ymin, ymax)
crs : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +datum=WGS84 +units=km +no_defs
source : memory
names : v
values : -7.516604e-15, 520.156 (min, max)4.3.3 Overlaying KDE layer on tmap plot
To further explore the map, we will now be overlaying the KDE layer both onto OpenStreetMap of Singapore, and also on the Singapore Planning Area layer and OSM road layer that we have pre-processed.
4.3.3.1 Overlay on OpenStreetMap
Show code
tmap_mode("view")
tm_basemap(leaflet::providers$OpenStreetMap) +
tm_shape(kde_originSG_500_raster) +
tm_raster("v", alpha = 0.7,
palette = "YlOrRd") +
tm_layout(legend.position = c("right", "bottom"), frame = FALSE)Show code
tmap_mode("plot")Show code
tmap_mode("view")
tm_basemap(leaflet::providers$OpenStreetMap) +
tm_shape(kde_destSG_500_raster) +
tm_raster("v", alpha = 0.7,
palette = "YlOrRd") +
tm_layout(legend.position = c("right", "bottom"), frame = FALSE)Show code
tmap_mode("plot")As you can see from the plot, there are certain planning areas that are hotspots for hailing of Grab ride service, in particular Central Region (Orchard, Newton etc), Woodlands, Punggol, Tampines, and most notably Changi (where the airport lies).
4.3.3.2 Overlay on Planning Area and OSM Road Layers
To further confirm our observation, let’s plot the KDE layer over our Planning Area and OSM Road Layers.
Show code
tmap_mode("view")
tm_shape(mpsz2019_new) +
tm_polygons("PLN_AREA_N") +
tm_shape(kde_originSG_500_raster) +
tm_raster("v", alpha = 0.7,
palette = "YlOrRd")Show code
tmap_mode("view")
tm_shape(mpsz2019_new) +
tm_polygons("PLN_AREA_N") +
tm_shape(kde_destSG_500_raster) +
tm_raster("v", alpha = 0.7,
palette = "YlOrRd")The common overlapping Planning Areas include “TAMPINES”, “CHANGI”, “WOODLANDS”, and “NOVENA”, so let’s do a further analysis on these areas.
4.4 In-depth KDE Computation
4.4.1 Data Preparation
To do in-depth KDE computation on these 4 planning areas, we will first need to extract their respective boundaries. In the code below, we extracted their boundaries and converted them to sp’s Spatial* class.
Show code
mpsz <- as_Spatial(mpsz2019_new)
cg = mpsz[mpsz@data$PLN_AREA_N == "CHANGI",]
tp = mpsz[mpsz@data$PLN_AREA_N == "TAMPINES",]
wl = mpsz[mpsz@data$PLN_AREA_N == "WOODLANDS",]
nv = mpsz[mpsz@data$PLN_AREA_N == "NOVENA",]Plotting down these boundaries.
Show code
par(mfrow=c(2,2))
plot(cg, main = "CHANGI")
plot(tp, main = "TAMPINES")
plot(wl, main = "WOODLANDS")
plot(nv, main = "NOVENA")
Turning the spatial point data frame into generic sp format, then into owin object as done previously.
Show code
cg_sp = as(cg, "SpatialPolygons")
tp_sp = as(tp, "SpatialPolygons")
wl_sp = as(wl, "SpatialPolygons")
nv_sp = as(nv, "SpatialPolygons")
cg_owin = as(cg_sp, "owin")
tp_owin = as(tp_sp, "owin")
wl_owin = as(wl_sp, "owin")
nv_owin = as(nv_sp, "owin")By using the code below, we will be able to extract grab origin and destination points for these specific areas.
Show code
origin_cg_ppp = origin_ppp[cg_owin]
origin_tp_ppp = origin_ppp[tp_owin]
origin_wl_ppp = origin_ppp[wl_owin]
origin_nv_ppp = origin_ppp[nv_owin]
dest_cg_ppp = dest_ppp[cg_owin]
dest_tp_ppp = dest_ppp[tp_owin]
dest_wl_ppp = dest_ppp[wl_owin]
dest_nv_ppp = dest_ppp[nv_owin]Next up is the rescale() function used previously as well.
Show code
origin_cg_ppp.km = rescale(origin_cg_ppp, 1000, "km")
origin_tp_ppp.km = rescale(origin_tp_ppp, 1000, "km")
origin_wl_ppp.km = rescale(origin_wl_ppp, 1000, "km")
origin_nv_ppp.km = rescale(origin_nv_ppp, 1000, "km")
dest_cg_ppp.km = rescale(dest_cg_ppp, 1000, "km")
dest_tp_ppp.km = rescale(dest_tp_ppp, 1000, "km")
dest_wl_ppp.km = rescale(dest_wl_ppp, 1000, "km")
dest_nv_ppp.km = rescale(dest_nv_ppp, 1000, "km")Finally, we plot the four planning areas and the grab hailing origin and destination points
Show code
par(mfrow=c(2,4))
plot(origin_cg_ppp.km, main = "CHANGI ORIGIN")
plot(origin_tp_ppp.km, main = "TAMPINES ORIGIN")
plot(origin_wl_ppp.km, main = "WOODLANDS ORIGIN")
plot(origin_nv_ppp.km, main = "NOVENA ORIGIN")
plot(dest_cg_ppp.km, main = "CHANGI DESTINATION")
plot(dest_tp_ppp.km, main = "TAMPINES DESTINATION")
plot(dest_wl_ppp.km, main = "WOODLANDS DESTINATION")
plot(dest_nv_ppp.km, main = "NOVENA DESTINATION")
4.4.2 Computing KDE
We will now be computing the KDE of each planning area using the fixed bandwidth method.
Show code
par(mfrow=c(1,2))
plot(density(origin_cg_ppp.km,
sigma=0.5,
edge=TRUE,
kernel="gaussian"),
main="Changi Origin")
plot(density(dest_cg_ppp.km,
sigma=0.5,
edge=TRUE,
kernel="gaussian"),
main="Changi Destination")
Show code
tmap_mode('plot')
tm_shape(mpsz2019_new[mpsz2019_new$PLN_AREA_N == "CHANGI", ]) +
tm_polygons('SUBZONE_N')
The hotspot in Changi area is centered around Changi Airport, indicating a likely surge in use of Grab services due to the constant flow of passengers arriving and departing from Singapore.
Show code
par(mfrow=c(1,2))
plot(density(origin_tp_ppp.km,
sigma=0.5,
edge=TRUE,
kernel="gaussian"),
main="Tampines Origin")
plot(density(dest_tp_ppp.km,
sigma=0.5,
edge=TRUE,
kernel="gaussian"),
main="Tampines Destination")
Show code
tmap_mode('plot')
tm_shape(mpsz2019_new[mpsz2019_new$PLN_AREA_N == "TAMPINES", ]) +
tm_polygons('SUBZONE_N')
The hotspot in Tampines area is mainly concentrated around the stretch from Tampines West to Tampines East, encompassing the bulk of where most residents of Tampines currently live (Tampines West, Tampines, Tampines East).
Show code
par(mfrow=c(1,2))
plot(density(origin_wl_ppp.km,
sigma=0.5,
edge=TRUE,
kernel="gaussian"),
main="Woodlands Origin")
plot(density(dest_wl_ppp.km,
sigma=0.5,
edge=TRUE,
kernel="gaussian"),
main="Woodlands Destination")
Show code
tmap_mode('plot')
tm_shape(mpsz2019_new[mpsz2019_new$PLN_AREA_N == "WOODLANDS", ]) +
tm_polygons('SUBZONE_N')
The rides are concentrated around the lower half of Woodlands area, ranging from Woodlands West to Woodlands South, then Woodlands East. However, one prominent hotspot shared across both the origin and destination map is the Woodlands West region, indicating that this might either be the area with the wealthiest residents in Woodlands, or that there are just more residents concentrated here.
Show code
par(mfrow=c(1,2))
plot(density(origin_nv_ppp.km,
sigma=0.5,
edge=TRUE,
kernel="gaussian"),
main="Novena Origin")
plot(density(dest_nv_ppp.km,
sigma=0.5,
edge=TRUE,
kernel="gaussian"),
main="Novena Destination")
Show code
tmap_mode('plot')
tm_shape(mpsz2019_new[mpsz2019_new$PLN_AREA_N == "NOVENA", ]) +
tm_polygons('SUBZONE_N')
The Novena area’s notable hotspots present an interesting distinction. Origin points predominantly converge around the affluent Moulmein area, revealing a concentration in the wealthier section of town. Conversely, the destination points gravitate towards the Malcolm area, characterized by a cluster of prestigious schools, as illustrated in the figure below.
4.5 Nearest Neighbour Analysis
In this section, we will perform the Clark-Evans test of aggregation for a spatial point pattern by using clarkevans.test() of statspat package, to test whether the distribution of Grab ride hailing origin points are randomly distributed.
Using 95% confidence interval, the test hypotheses are:
Ho = The distribution of Grab ride hailing origin points are randomly distributed.
H1= The distribution of Grab ride hailing origin points are not randomly distributed.
For this section, we will be making use of the ppp object.
Clark-Evans Test
Show code
clarkevans.test(originSG_ppp,
correction="none",
clipregion="sg_owin",
alternative=c("clustered"),
nsim=99)
Clark-Evans test
No edge correction
Z-test
data: originSG_ppp
R = 0.28008, p-value < 2.2e-16
alternative hypothesis: clustered (R < 1)Show code
clarkevans.test(origin_cg_ppp,
correction="none",
clipregion="sg_owin",
alternative=c("clustered"),
nsim=99)
Clark-Evans test
No edge correction
Z-test
data: origin_cg_ppp
R = 0.11647, p-value < 2.2e-16
alternative hypothesis: clustered (R < 1)Show code
clarkevans.test(origin_tp_ppp,
correction="none",
clipregion="sg_owin",
alternative=c("clustered"),
nsim=99)
Clark-Evans test
No edge correction
Z-test
data: origin_tp_ppp
R = 0.31668, p-value < 2.2e-16
alternative hypothesis: clustered (R < 1)Show code
clarkevans.test(origin_wl_ppp,
correction="none",
clipregion="sg_owin",
alternative=c("clustered"),
nsim=99)
Clark-Evans test
No edge correction
Z-test
data: origin_wl_ppp
R = 0.31908, p-value < 2.2e-16
alternative hypothesis: clustered (R < 1)Show code
clarkevans.test(origin_nv_ppp,
correction="none",
clipregion="sg_owin",
alternative=c("clustered"),
nsim=99)
Clark-Evans test
No edge correction
Z-test
data: origin_nv_ppp
R = 0.35838, p-value < 2.2e-16
alternative hypothesis: clustered (R < 1)Having performed the Clark-Evans Test on all 4 planning area and Singapore as a whole, all of their p-values are <2.2e-16 < 0.05, thus we reject Ho. This means that the distribution of Grab ride hailing origin points are not randomly distributed which we explored in earlier sections.
Furthermore, as their R value ranges from 0.11647 to 0.35838 which is <1, this suggests that the points are clustering.
5. Network Kernel Density Estimation (NKDE) Layers
In this section, we will be using appropriate functions of spNetwork package:
- to derive network constrained kernel density estimation (NetKDE), and
- to perform network G-function and k-function analysis,
where in this case the network refers to OSM’s Road Map of Singapore.
However, due to limitations in computational power, we will be limiting the area of scope down to the 4 areas identified in the previous section, Changi, Tampines, Woodlands, and Novena, and only the Origin points.
5.1 Data Preparation
5.1.1 Initial Data Pre-processing
Before we begin, let us first convert our sg_roads_new data from SFC_GEOMETRY to SFC_LINESTRING.
Show code
sg_roads_linestring <- st_cast(sg_roads_new, "LINESTRING")5.1.2 Narrowing down the scope
Then, let us narrow down the scope of our data to the 4 areas mentioned.
Show code
# Roads
cg_roads <- sg_roads_linestring %>% filter(PLN_AREA_N == "CHANGI")
tp_roads <- sg_roads_linestring %>% filter(PLN_AREA_N == "TAMPINES")
wl_roads <- sg_roads_linestring %>% filter(PLN_AREA_N == "WOODLANDS")
nv_roads <- sg_roads_linestring %>% filter(PLN_AREA_N == "NOVENA")
# Grab Origin Points
cg_origin <- origin_sf_new %>% filter(PLN_AREA_N == "CHANGI")
tp_origin <- origin_sf_new %>% filter(PLN_AREA_N == "TAMPINES")
wl_origin <- origin_sf_new %>% filter(PLN_AREA_N == "WOODLANDS")
nv_origin <- origin_sf_new %>% filter(PLN_AREA_N == "NOVENA")5.1.3 Visualising the data
Before we begin our analysis, let us visualise our geospatial data to make sure everything falls into place.
Show code
tm_shape(cg_roads) +
tm_lines("PLN_AREA_N") +
tm_shape(cg_origin) +
tm_dots()
Show code
tm_shape(tp_roads) +
tm_lines("PLN_AREA_N") +
tm_shape(tp_origin) +
tm_dots()
Show code
tm_shape(wl_roads) +
tm_lines("PLN_AREA_N") +
tm_shape(wl_origin) +
tm_dots()
Show code
tm_shape(nv_roads) +
tm_lines("PLN_AREA_N") +
tm_shape(nv_origin) +
tm_dots()
5.2 Network Constrained KDE (NetKDE) Analysis
We will now perform NetKDE analysis by using appropriate functions provided in spNetwork package.
5.2.1 Preparing the lixels objects
Before we can compute NetKDE, the SpatialLines object need to be cut into lixels with a specified minimal distance, and this can be done using lixelize_lines() of spNetwork package.
Show code
cg_lixels <- lixelize_lines(cg_roads,
700,
mindist = 350)
tp_lixels <- lixelize_lines(tp_roads,
700,
mindist = 350)
wl_lixels <- lixelize_lines(wl_roads,
700,
mindist = 350)
nv_lixels <- lixelize_lines(nv_roads,
700,
mindist = 350)5.2.2 Generating line centre points
Next, we will use lines_center() of spNetwork to generate a SpatialPointsDataFrame (i.e. samples) with line centre points.
Show code
cg_lines_center <- lines_center(cg_lixels)
tp_lines_center <- lines_center(tp_lixels)
wl_lines_center <- lines_center(wl_lixels)
nv_lines_center <- lines_center(nv_lixels)5.2.3 Computing NetKDE
We are now ready to compute NetKDE. As the code is fairly long, we will split it into 4 tabs.
Show code
# Origin
cg_o_densities <- nkde(cg_roads,
events = cg_origin,
w = rep(1,nrow(cg_origin)),
samples = cg_lines_center,
kernel_name = "quartic", # kernel method
bw = 300,
div= "bw",
method = "simple",
# method used to calculate NKDE. spNetwork supports 3 popular methods, namely simple, discontinuous, and continuous
digits = 1,
tol = 1,
grid_shape = c(1,1),
max_depth = 8,
agg = 5,
# we aggregate events within a 5m radius (faster calculation)
sparse = TRUE,
verbose = FALSE)Show code
tp_o_densities <- nkde(tp_roads,
events = tp_origin,
w = rep(1,nrow(tp_origin)),
samples = tp_lines_center,
kernel_name = "quartic", # kernel method
bw = 300,
div= "bw",
method = "simple",
# method used to calculate NKDE. spNetwork supports 3 popular methods, namely simple, discontinuous, and continuous
digits = 1,
tol = 1,
grid_shape = c(1,1),
max_depth = 8,
agg = 5,
# we aggregate events within a 5m radius (faster calculation)
sparse = TRUE,
verbose = FALSE)Show code
wl_o_densities <- nkde(wl_roads,
events = wl_origin,
w = rep(1,nrow(wl_origin)),
samples = wl_lines_center,
kernel_name = "quartic", # kernel method
bw = 300,
div= "bw",
method = "simple",
# method used to calculate NKDE. spNetwork supports 3 popular methods, namely simple, discontinuous, and continuous
digits = 1,
tol = 1,
grid_shape = c(1,1),
max_depth = 8,
agg = 5,
# we aggregate events within a 5m radius (faster calculation)
sparse = TRUE,
verbose = FALSE)Show code
nv_o_densities <- nkde(nv_roads,
events = nv_origin,
w = rep(1,nrow(nv_origin)),
samples = nv_lines_center,
kernel_name = "quartic", # kernel method
bw = 300,
div= "bw",
method = "simple",
# method used to calculate NKDE. spNetwork supports 3 popular methods, namely simple, discontinuous, and continuous
digits = 1,
tol = 1,
grid_shape = c(1,1),
max_depth = 8,
agg = 5,
# we aggregate events within a 5m radius (faster calculation)
sparse = TRUE,
verbose = FALSE)5.2.4 Reinsert Density
Before we are able to visualise, we first need to insert the computed values back into lines_center and lixels objects as density field.
Show code
cg_lines_center$o_density <- cg_o_densities
cg_lixels$o_density <- cg_o_densities
tp_lines_center$o_density <- tp_o_densities
tp_lixels$o_density <- tp_o_densities
wl_lines_center$o_density <- wl_o_densities
wl_lixels$o_density <- wl_o_densities
nv_lines_center$o_density <- nv_o_densities
nv_lixels$o_density <- nv_o_densitiesSince svy21 projection system is in meter, the computed density values are very small i.e. 0.0000005. We will thus need to rescale the density values from number of events per meter to number of events per kilometer.
Show code
cg_lines_center$o_density <- cg_lines_center$o_density*1000
cg_lixels$o_density <- cg_lixels$o_density*1000
tp_lines_center$o_density <- tp_lines_center$o_density*1000
tp_lixels$o_density <- tp_lixels$o_density*1000
wl_lines_center$o_density <- wl_lines_center$o_density*1000
wl_lixels$o_density <- wl_lixels$o_density*1000
nv_lines_center$o_density <- nv_lines_center$o_density*1000
nv_lixels$o_density <- nv_lixels$o_density*10005.2.5 Visualising NetKDE
Show code
tmap_mode('view')
tm_basemap(leaflet::providers$OpenStreetMap) +
tm_shape(cg_lixels)+
tm_lines(col="o_density")+
tm_shape(cg_origin)+
tm_dots(alpha=0.2)Show code
tmap_mode('plot')This tmap plot further reinforces our observation above that the grab ride traffic are from incoming tourists or locals returning home form the airport, as you can see the denser area being the Changi Airport Terminals. However, it is worth highlighting that there some slight traffic along the Changi Village area and infront of the Japanese School as well.
Show code
tmap_mode('view')
tm_basemap(leaflet::providers$OpenStreetMap) +
tm_shape(tp_lixels)+
tm_lines(col="o_density")+
tm_shape(tp_origin)+
tm_dots(alpha=0.2)Show code
tmap_mode('plot')As we have discovered earlier, a huge portion of the grab rides indeed originated from Tampines East, one of the more populated area of Tampines. Particularly along Tampines Avenue 2, there seems to be a higher density, presumably due to it being more convenient to get a ride along the main road.
Surprisingly, the other higher density area in this network density map is the area around Changi General Hospital.
Show code
tmap_mode('view')
tm_basemap(leaflet::providers$OpenStreetMap) +
tm_shape(wl_lixels)+
tm_lines(col="o_density")+
tm_shape(wl_origin)+
tm_dots(alpha=0.2)Show code
tmap_mode('plot')There are 3 main points of to focus on with higher density, mainly:
Along the route to Woodlands Checkpoint, showing that a significant portion of the rides in Woodlands are people coming in from Malaysia.
Around the main hub of Woodlands, along the Woodlands MRT stretch. No surprises here, as the area is perhaps the most dense in terms of human traffic due to concentration of malls, bus interchange, and MRT station.
3 different points around the Sembawang Air Base, which I assume is the entrance. This make sense as well, as military bases in Singapore are generally more inaccessible.
Show code
tmap_mode('view')
tm_basemap(leaflet::providers$OpenStreetMap) +
tm_shape(nv_lixels)+
tm_lines(col="o_density")+
tm_shape(nv_origin)+
tm_dots(alpha=0.2)Show code
tmap_mode('plot')Network KDE indicates that the majority of the traffic is along Moulmein Road, which is the main road next to several of the moderately wealthier estates in Singapore.
5.3 Network Constrained G- and K-Function Analysis
We are now going to perform complete spatial randomness (CSR) test by using kfunctions() of spNetwork package. The null hypothesis is defined as:
- The observed spatial point events (i.e distribution of Grab ride hailing points) are uniformly distributed over a street network in the 4 Planning Area specified above.
The CSR test is based on the assumption of the binomial point process which implies the hypothesis that the childcare centres are randomly and independently distributed over the street network.
If this hypothesis is rejected, we may infer that the distribution of Grab ride hailing points are spatially interacting and dependent on each other; as a result, they may form nonrandom patterns.
Show code
kfun_cg <- kfunctions(cg_roads,
cg_origin[c("trj_id","PLN_AREA_N", "geometry")],
start = 0,
# A double, the start value for evaluating the k and g functions.
end = 1000,
# A double, the last value for evaluating the k and g functions.
step = 50,
# A double, the jump between two evaluations of the k and g function
width = 50,
# The width of each donut for the g-function
nsim = 50,
# number of Monte Carlo simulations required.
resolution = 50,
verbose = FALSE,
agg = 5,
conf_int = 0.05
# A double indicating the width confidence interval (default = 0.05).
)
kfun_cg$plotk
$plotg
$values
obs_k lower_k upper_k obs_g lower_g upper_g distances
1 0.00000 0.000000 0.000000 14.86145 0.4666575 0.740489 0
2 36.75689 2.027349 2.523346 43.40340 2.4061742 2.891653 50
3 79.23030 4.792789 5.565200 41.90507 2.8875935 3.651886 100
4 121.95096 7.772828 9.299568 42.98638 3.2411387 4.192353 150
5 166.35078 11.183174 13.675336 47.23408 3.7570638 4.744259 200
6 214.49640 15.486794 18.645635 49.64395 4.2047155 5.230292 250
7 264.37654 20.154292 23.852124 49.89859 4.4174700 5.863389 300
8 316.81416 24.897813 29.586347 52.19405 4.9863531 6.185934 350
9 369.73892 30.336800 35.754382 54.99879 5.4725700 6.650008 400
10 423.85570 36.210522 42.276517 52.66643 5.9375669 7.328313 450
11 477.36355 42.523223 49.538771 55.36046 6.2578980 7.861399 500
12 531.87151 49.280255 57.486527 54.89546 6.7404245 8.309235 550
13 587.96637 56.497301 65.593894 55.21653 7.4170687 8.740465 600
14 643.18290 64.142624 74.543792 55.12058 7.7444117 9.705333 650
15 698.60978 72.400193 84.237315 53.94332 7.8931369 10.119955 700
16 749.05825 80.872731 94.775583 53.11297 8.3433720 10.749731 750
17 802.93515 89.886289 105.839187 49.17157 8.6216320 11.201995 800
18 852.30969 99.165744 117.035277 50.21966 9.2870203 11.736557 850
19 900.47377 108.683048 128.865019 47.30420 9.5591911 12.188084 900
20 946.48263 118.736944 141.281357 45.53648 10.1415443 12.501588 950
21 991.41387 129.521181 154.070062 42.06376 10.3893581 12.972305 1000The blue line represents the empirical network K-function of the Grab ride hailing origin points in Changi planning area. The gray envelop represents the results of the 50 simulations in the interval 2.5% - 97.5%. Because the blue line is above the gray area, we can infer that these origin points in Changi planning area are in clusters, which reinforces our observations made above.
Show code
kfun_tp <- kfunctions(tp_roads,
tp_origin[c("trj_id","PLN_AREA_N", "geometry")],
start = 0,
# A double, the start value for evaluating the k and g functions.
end = 1000,
# A double, the last value for evaluating the k and g functions.
step = 50,
# A double, the jump between two evaluations of the k and g function
width = 50,
# The width of each donut for the g-function
nsim = 50,
# number of Monte Carlo simulations required.
resolution = 50,
verbose = FALSE,
agg = 10,
conf_int = 0.05
# A double indicating the width confidence interval (default = 0.05).
)
kfun_tp$plotk
$plotg
$values
obs_k lower_k upper_k obs_g lower_g upper_g distances
1 0.00000 0.000000 0.000000 3.544369 0.5025507 0.6765691 0
2 10.43806 1.663994 1.949098 14.896712 1.8908889 2.4198014 50
3 26.84334 3.859644 4.487299 17.212894 2.4333632 3.0265803 100
4 44.52556 6.707482 7.766983 17.877273 3.0079899 3.7823872 150
5 63.16474 10.183280 11.728418 19.330982 3.6828828 4.5352990 200
6 82.52925 14.177476 16.721010 18.724508 4.2366333 5.3691248 250
7 102.07051 19.084583 22.442896 20.833454 5.0983447 6.3643214 300
8 122.50168 24.557936 28.783141 20.988882 5.7032951 7.1440521 350
9 144.84065 30.848658 36.368639 22.921066 6.6305300 8.1269059 400
10 167.65810 37.932672 44.903010 22.872304 7.6156696 9.3011496 450
11 190.66450 46.125559 54.745720 23.615920 8.4697619 10.3231652 500
12 215.64575 55.318213 65.469494 25.459724 9.3695684 11.6689892 550
13 241.81557 65.555131 77.495815 26.828100 10.6097928 12.8008711 600
14 268.85090 76.502904 90.551618 27.711907 11.5681134 14.1105810 650
15 297.63862 88.618368 105.227226 29.711138 12.6369098 15.4402527 700
16 329.33375 101.950742 120.940240 31.756083 13.4551927 16.5547632 750
17 360.65707 116.363036 138.147344 32.469224 14.7775502 17.8976920 800
18 393.75715 131.554452 156.461644 35.346167 15.5731284 18.8945649 850
19 429.61227 147.849101 175.995445 35.693594 16.4439826 20.2742173 900
20 466.66205 164.802492 196.295414 39.131297 17.3419606 21.1603096 950
21 506.48820 182.834888 217.578542 39.405581 18.5294613 22.3173343 1000Similar to Changi planning area, as the blue line is above the grey area, we can infer that the Tampines planning area consists of mainly origin points in clusters.
Show code
kfun_wl <- kfunctions(wl_roads,
wl_origin[c("trj_id","PLN_AREA_N", "geometry")],
start = 0,
# A double, the start value for evaluating the k and g functions.
end = 1000,
# A double, the last value for evaluating the k and g functions.
step = 50,
# A double, the jump between two evaluations of the k and g function
width = 50,
# The width of each donut for the g-function
nsim = 50,
# number of Monte Carlo simulations required.
resolution = 50,
verbose = FALSE,
agg = 5,
conf_int = 0.05
# A double indicating the width confidence interval (default = 0.05).
)
kfun_wl$plotk
$plotg
$values
obs_k lower_k upper_k obs_g lower_g upper_g distances
1 0.00000 0.000000 0.000000 12.80481 0.8216355 1.190549 0
2 30.08326 2.714612 3.297066 35.47919 3.4162738 4.204424 50
3 67.77057 6.716210 8.054094 39.66965 4.5038788 5.566418 100
4 108.36633 11.943373 13.909825 41.09708 5.5168593 6.555863 150
5 149.89203 17.996763 21.344536 42.13034 6.4737756 8.191288 200
6 193.36561 25.287199 29.933056 44.11650 7.7433498 9.561701 250
7 237.89542 33.543163 40.506973 45.81948 9.1135714 11.721030 300
8 285.19973 43.569641 53.005820 47.25456 10.7419174 13.406588 350
9 332.27061 55.127071 67.588988 48.52127 12.0667902 15.233550 400
10 383.29084 68.030613 83.965442 52.89924 14.2974903 17.670520 450
11 437.26544 83.169256 102.457980 54.76294 15.8079678 19.981202 500
12 492.18911 100.285067 122.305433 55.70436 18.2502955 22.169041 550
13 547.99297 119.854303 144.802057 57.94692 19.9140403 24.668007 600
14 608.33170 141.177025 170.022094 60.72525 21.9834366 27.591376 650
15 669.64247 164.449171 197.927659 62.01109 24.3668300 29.650440 700
16 732.69448 189.755887 229.248732 64.49475 26.6007830 33.255954 750
17 799.31698 217.677334 263.172479 68.91482 28.5394906 36.061454 800
18 871.09049 247.861052 300.575453 73.95102 31.7822144 38.662407 850
19 945.99058 280.115123 340.905241 76.33518 33.3556444 41.635908 900
20 1024.72905 314.307710 383.040559 81.14559 35.3934688 42.709736 950
21 1109.01653 351.072740 427.204709 86.31191 37.4701361 45.535137 1000Similar to Changi planning area, as the blue line is above the grey area, we can infer that the Woodlands planning area consists of mainly origin points in clusters.
Show code
kfun_nv <- kfunctions(nv_roads,
nv_origin[c("trj_id","PLN_AREA_N", "geometry")],
start = 0,
# A double, the start value for evaluating the k and g functions.
end = 1000,
# A double, the last value for evaluating the k and g functions.
step = 50,
# A double, the jump between two evaluations of the k and g function
width = 50,
# The width of each donut for the g-function
nsim = 50,
# number of Monte Carlo simulations required.
resolution = 50,
verbose = FALSE,
agg = 5,
conf_int = 0.05
# A double indicating the width confidence interval (default = 0.05).
)
kfun_nv$plotk
$plotg
$values
obs_k lower_k upper_k obs_g lower_g upper_g distances
1 0.000000 0.0000000 0.0000000 2.070894 0.09225552 0.1942221 0
2 4.899254 0.3226515 0.5243998 5.799959 0.43432927 0.6678814 50
3 10.932279 0.8271436 1.1927667 5.931059 0.54709950 0.8759419 100
4 16.831777 1.4766952 2.0144478 5.841231 0.70624027 0.9963596 150
5 22.590463 2.3080874 3.1064618 6.110714 0.87836965 1.1944662 200
6 28.919677 3.4388245 4.4197677 6.732225 1.08934345 1.5553552 250
7 36.047630 4.7541939 6.0819451 7.460558 1.33964724 1.8186476 300
8 43.869927 6.2384153 8.0355771 8.091780 1.61422880 2.1687330 350
9 52.602640 8.0705371 10.3696417 8.999769 1.80262428 2.4826446 400
10 61.765070 10.0171285 13.0232018 9.453763 2.00485809 2.9185519 450
11 70.978483 12.1084154 15.9646961 9.152719 2.26001743 2.9984257 500
12 80.264729 14.4423587 19.1615926 9.232835 2.49004928 3.3141581 550
13 89.281492 17.1032021 22.6253018 9.490180 2.63134590 3.5617914 600
14 99.725788 20.0541648 26.4664089 10.415163 2.97754687 3.9319059 650
15 110.301184 23.1637827 30.3906673 11.077946 3.30505396 4.1990828 700
16 121.383985 26.5987227 34.8417533 11.230896 3.55377969 4.6740773 750
17 132.750836 30.2589604 39.4643618 11.612056 3.73695545 4.8937911 800
18 144.807176 34.0448356 44.4415470 11.956801 3.98871591 5.2097663 850
19 156.674149 38.3231853 49.9291723 12.357384 4.12236502 5.5197934 900
20 169.582638 42.8815790 55.6176962 13.102711 4.45958322 5.5540250 950
21 182.794599 47.4041630 61.2769653 13.190111 4.60039428 5.8913646 1000Similar to Changi planning area, as the blue line is above the grey area, we can infer that the Novena planning area consists of mainly origin points in clusters.
The results of our G- and K-Function Analysis on all four planning area shows a spatial pattern of clustering among the grab origin points, which supports the idea that grab rides are commonly booked at the same location within an area, possibly due to designated pickup points or taxi stands.
6. Conclusion
In conclusion, our analysis of Grab ride-hailing origin points in the specific planning areas of Changi, Tampines, Woodlands, and Novena, and also the whole of Singapore uncovered noteworthy spatial patterns. The observed clustering of origin points within these areas suggests a localized preference for specific pickup locations, potentially driven by factors such as designated pickup points, popular landmarks, transportation hubs, or simply area with higher population density.
These findings hold practical implications for both Grab and urban planners as the identified clusters can guide Grab in optimizing their service by strategically placing vehicles or promoting the use of specific pickup points, ultimately enhancing the efficiency and user experience. Urban planners, on the other hand, can leverage this information to make informed decisions regarding infrastructure development, such as improving the accessibility of popular pickup locations or adjusting traffic flow in areas with high ride-hailing activity.
Moreover, understanding the spatial dynamics of Grab ride-hailing services contributes to a broader perspective on urban mobility patterns. This knowledge can be valuable for city officials, transportation authorities, and policymakers in crafting policies that support sustainable and efficient transportation solutions. By aligning urban planning efforts with the observed ride-hailing patterns, cities can work towards creating more resilient, user-friendly, and accessible transportation systems.
In essence, our analysis not only sheds light on the localized behaviors of Grab users but also opens avenues for strategic decision-making that can enhance the overall urban mobility landscape. As technology continues to shape the future of transportation, such spatial insights play a crucial role in fostering innovation and creating urban environments that are responsive to the evolving needs of their residents.